scholarly journals EEG-based brain–computer interfaces exploiting steady-state somatosensory-evoked potentials: a literature review

2021 ◽  
Vol 18 (5) ◽  
pp. 051003
Author(s):  
Jimmy Petit ◽  
José Rouillard ◽  
François Cabestaing
Keyword(s):  
Steady State ◽  
Brain Activity ◽  
Somatosensory System ◽  
Classification Performance ◽  
Brain Response ◽  
Somatosensory Stimulation ◽  
External Stimulation ◽  
Sustained Change ◽  
External Stimuli ◽  
The Brain

Abstract A brain–computer interface (BCI) aims to derive commands from the user’s brain activity in order to relay them to an external device. To do so, it can either detect a spontaneous change in the mental state, in the so-called ‘active’ BCIs, or a transient or sustained change in the brain response to an external stimulation, in ‘reactive’ BCIs. In the latter, external stimuli are perceived by the user through a sensory channel, usually sight or hearing. When the stimulation is sustained and periodical, the brain response reaches an oscillatory steady-state that can be detected rather easily. We focus our attention on electroencephalography-based BCIs (EEG-based BCI) in which a periodical signal, either mechanical or electrical, stimulates the user skin. This type of stimulus elicits a steady-state response of the somatosensory system that can be detected in the recorded EEG. The oscillatory and phase-locked voltage component characterising this response is called a steady-state somatosensory-evoked potential (SSSEP). It has been shown that the amplitude of the SSSEP is modulated by specific mental tasks, for instance when the user focuses their attention or not to the somatosensory stimulation, allowing the translation of this variation into a command. Actually, SSSEP-based BCIs may benefit from straightforward analysis techniques of EEG signals, like reactive BCIs, while allowing self-paced interaction, like active BCIs. In this paper, we present a survey of scientific literature related to EEG-based BCI exploiting SSSEP. Firstly, we endeavour to describe the main characteristics of SSSEPs and the calibration techniques that allow the tuning of stimulation in order to maximise their amplitude. Secondly, we present the signal processing and data classification algorithms implemented by authors in order to elaborate commands in their SSSEP-based BCIs, as well as the classification performance that they evaluated on user experiments.

scholarly journals Fractional Diffusion Based Modelling and Prediction of Human Brain Response to External Stimuli

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Hamidreza Namazi ◽  
Vladimir V. Kulish
Keyword(s):  
Human Brain ◽  
Brain Research ◽  
Brain Activity ◽  
Fractional Diffusion ◽  
Eeg Signal ◽  
Brain Response ◽  
Human Eeg ◽  
External Stimuli ◽  
The Brain ◽  
Good Agreement

Human brain response is the result of the overall ability of the brain in analyzing different internal and external stimuli and thus making the proper decisions. During the last decades scientists have discovered more about this phenomenon and proposed some models based on computational, biological, or neuropsychological methods. Despite some advances in studies related to this area of the brain research, there were fewer efforts which have been done on the mathematical modeling of the human brain response to external stimuli. This research is devoted to the modeling and prediction of the human EEG signal, as an alert state of overall human brain activity monitoring, upon receiving external stimuli, based on fractional diffusion equations. The results of this modeling show very good agreement with the real human EEG signal and thus this model can be used for many types of applications such as prediction of seizure onset in patient with epilepsy.

scholarly journals Conditional Entropy: A Potential Digital Marker for Stress

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 286
Author(s):  
Soheil Keshmiri
Keyword(s):  
Resting State ◽  
Nearest Neighbor ◽  
Brain Activity ◽  
Solution Concept ◽  
Conditional Entropy ◽  
K Nearest Neighbor ◽  
Brain Response ◽  
Substantial Progress ◽  
Eeg Recordings ◽  
The Brain

Recent decades have witnessed a substantial progress in the utilization of brain activity for the identification of stress digital markers. In particular, the success of entropic measures for this purpose is very appealing, considering (1) their suitability for capturing both linear and non-linear characteristics of brain activity recordings and (2) their direct association with the brain signal variability. These findings rely on external stimuli to induce the brain stress response. On the other hand, research suggests that the use of different types of experimentally induced psychological and physical stressors could potentially yield differential impacts on the brain response to stress and therefore should be dissociated from more general patterns. The present study takes a step toward addressing this issue by introducing conditional entropy (CE) as a potential electroencephalography (EEG)-based resting-state digital marker of stress. For this purpose, we use the resting-state multi-channel EEG recordings of 20 individuals whose responses to stress-related questionnaires show significantly higher and lower level of stress. Through the application of representational similarity analysis (RSA) and K-nearest-neighbor (KNN) classification, we verify the potential that the use of CE can offer to the solution concept of finding an effective digital marker for stress.

Prestimulus Neural Oscillations Inhibit Visual Perception via Modulation of Response Gain

2014 ◽  
Vol 26 (11) ◽  
pp. 2514-2529 ◽  
Author(s):  
Maximilien Chaumon ◽  
Niko A. Busch
Keyword(s):  
Brain Activity ◽  
Sensory Information ◽  
Inhibitory Effect ◽  
Qualitative Description ◽  
Alpha Power ◽  
Alpha Oscillations ◽  
External Stimuli ◽  
Functional Mechanisms ◽  
The Brain ◽  
Output Transformation

The ongoing state of the brain radically affects how it processes sensory information. How does this ongoing brain activity interact with the processing of external stimuli? Spontaneous oscillations in the alpha range are thought to inhibit sensory processing, but little is known about the psychophysical mechanisms of this inhibition. We recorded ongoing brain activity with EEG while human observers performed a visual detection task with stimuli of different contrast intensities. To move beyond qualitative description, we formally compared psychometric functions obtained under different levels of ongoing alpha power and evaluated the inhibitory effect of ongoing alpha oscillations in terms of contrast or response gain models. This procedure opens the way to understanding the actual functional mechanisms by which ongoing brain activity affects visual performance. We found that strong prestimulus occipital alpha oscillations—but not more anterior mu oscillations—reduce performance most strongly for stimuli of the highest intensities tested. This inhibitory effect is best explained by a divisive reduction of response gain. Ongoing occipital alpha oscillations thus reflect changes in the visual system's input/output transformation that are independent of the sensory input to the system. They selectively scale the system's response, rather than change its sensitivity to sensory information.

FRACTAL-BASED ANALYSIS OF THE INFLUENCE OF VARIATIONS OF RHYTHMIC PATTERNS OF MUSIC ON HUMAN BRAIN RESPONSE

Fractals ◽  
2018 ◽  
Vol 26 (05) ◽  
pp. 1850080 ◽  
Author(s):  
ZHALEH MOHAMMAD ALIPOUR ◽  
REZA KHOSROWABADI ◽  
HAMIDREZA NAMAZI
Keyword(s):  
Human Brain ◽  
Human Behavior ◽  
Fractal Structure ◽  
Brain Activity ◽  
Chaotic Signal ◽  
Auditory Stimuli ◽  
Eeg Signal ◽  
Brain Response ◽  
Human Eeg ◽  
The Brain

Analysis of human behavior is one of the major research topics in neuroscience. It is known that human behavior is related to his brain activity. In this way, the analysis of human brain activity is the root for analysis of his behavior. Electroencephalography (EEG) as one of the most famous methods for measuring of the brain activity generates a chaotic signal, which has fractal characteristic. This study reveals the relation between the fractal structure (complexity) of human EEG signal and the applied auditory stimuli. For this purpose, we chose a range of auditory stimuli with different rhythmic patterns. We demonstrated that the fractal structure of human EEG signal changes significantly based on different rhythmic patterns. The capability observed in this research can be applied to other kinds of stimuli in order to classify the brain response based on the types of stimuli.

scholarly journals Dissecting Salience Network Responsivity to Evoked Nociception and Pain: Model Free Analyses

2021 ◽  
Author(s):  
Lino Becerra ◽  
David Borsook
Keyword(s):  
Brain Activity ◽  
Cognitive Integration ◽  
Brain Response ◽  
Pain Model ◽  
Self Reflection ◽  
Model Free ◽  
Brain Changes ◽  
Diverting Activity ◽  
External Stimuli

Abstract Dissecting the responses of brain changes to acute nociceptive stimuli includes understanding networks that dynamically interact to produce the experience of pain. Among these networks the salience, default, sensory, and cognitive/central executive networks are known to interact in monitoring external stimuli, assigning salience and diverting activity from self-reflection (default mode) to cognitive integration and planning potential reaction to pain (executive). Several studies of acute evoked pain report a pattern of activity reflecting the involvement of these networks. However, it has also been proposed that much of the activity seems to reflect a response to the saliency of pain rather than pain/nociceptive processing itself. These results stem from the assumption that the evoked fMRI signal arises from a single, canonical hemodynamic response induced by the stimuli. Using a model-free analysis we demonstrate that the observed fMRI response has a complex, dynamic nature not captured by model-based analysis. We provide evidence that these and other networks have a distinct temporal response. Results presented here suggest that proper modeling and characterization of the brain response is fundamental for a correct interpretation of brain activity in response to phasic/evoked noxious stimuli.

Beyond the Passive/Active Dichotomy: A Spectrum Model of the Brain’s Neural Activities

2018 ◽  
pp. 3-26
Author(s):  
Georg Northoff
Keyword(s):  
Spontaneous Activity ◽  
Empirical Data ◽  
Resting State ◽  
Brain Activity ◽  
Empirical Support ◽  
Theoretical Issue ◽  
Active Model ◽  
External Stimuli ◽  
The Brain ◽  
Spectrum Model

Neuroscience has made considerable progress over the last decades in revealing neuronal mechanisms on different levels of brain activity including genetic, molecular, cellular, regional and network levels. However, despite all this progress, no particular model of the brain has commanded consensus. A model of the brain should attribute clear features to the brain, such as its degree of participation in its own processing of stimuli. While primarily a theoretical issue, models of the brain may create major reverberations within neuroscientific investigation and philosophical work on the mind-brain problem. Both philosophers and neuroscientists often presuppose a passive model of the brain wherein the brain passively receives and processes external stimuli. However, recent empirical data do not support a passive view of the brain. Accordingly, I will advocate for an active model of the brain. The empirical support for an active model of brain comes from findings concerning its resting state or spontaneous activity. Empirical data shows that the brain’s stimulus-induced activity results from the integration of spontaneous activity and external stimuli. However, the brain’s activity can vary with respect to the extent of integration of resting state activity and external stimuli. This leads me to suggest what I describe as a spectrum model of the brain. The spectrum model claims that stimulus-induced activity is based on a spectrum or continuum of different possible relationships or balances between spontaneous activity and external stimuli.

scholarly journals Single-Channel Selection for Detecting Steady-State Visual Evoked Potentials in a Brain-Computer Interface Speller

2021 ◽  
Author(s):  
Farzad Saffari ◽  
Ali Khadem
Keyword(s):  
Steady State ◽  
Information Transfer ◽  
Single Channel ◽  
Brain Computer Interface ◽  
Performance Criteria ◽  
Computer Interface ◽  
Brain Response ◽  
Brainstem Stroke ◽  
The Brain ◽  
Visual Evoked

Purpose: Brain-Computer Interface (BCI) provides a secondary communication pathway for patients with neuromuscular diseases such as amyotrophic lateral sclerosis (ALS) or brainstem stroke in which they are almost incapacitated to move or talk. BCI enacts neural oscillations to generate a command signal for machines to operate desired tasks instead of patients. Steady-State Visual Evoked Potential (SSVEP) is the brain response to a visual stimulus, with the same frequency as its eliciting signal (or its harmonics), that has been widely used in BCI environments. In order to provide a more convenient situation for BCI users, we aim to find the best single-channel EEG, which results in the highest accuracy for detecting SSVEP. Materials and Methods: We developed a Deep Convolutional Neural Network with single-channel EEG as input to classify a 40-class SSVEP; each class represents a stimulus, which has been acquired from 35 subjects. We used 3.5 s windows of the data (Trials of 3.5 seconds length for each class) to train our model and leave-one-subject-out cross-validation for the testing. Results: The proposed method resulted in the average classification accuracy of 74.30%±20.85 and Information Transfer Rate (ITR) of 57.51 bpm which outperforms the previous single-channel SSVEP BCIs in terms of ITR. Also, the O1 channel achieved the best performance criteria among the channels in the occipital and parietal lobes, which seems reasonable according to previous researches for finding the location of neurons, responsible for visual tasks in the brain. Conclusion: In this study, we dedicated our efforts to reduce the number of EEG channels to a single channel while proposing a deep learning strategy for an SSVEP-based BCI speller to make it more feasible for patients whose lives are dependent on such systems. The overall results, although not ideal, open a new promising window toward a feasible BCI system.

scholarly journals EEGG: An Analytic Brain-computer Interface Algorithm

2021 ◽  
Author(s):  
Gang Liu ◽  
Jing Wang
Keyword(s):  
Brain Activity ◽  
Brain Regions ◽  
Classification Performance ◽  
Data Driven ◽  
Computer Interface ◽  
Eeg Signals ◽  
Eeg Data ◽  
Data Driven Approach ◽  
Left And Right ◽  
The Brain

<div><div> <p><a></a></p><div> <p><a></a><a><i>Objective. </i></a>Modeling the brain as a white box is vital for investigating the brain. However, the physical properties of the human brain are unclear. Therefore, BCI algorithms using EEG signals are generally a data-driven approach and generate a black- or gray-box model. This paper presents the first EEG-based BCI algorithm (EEGBCI using Gang neurons, EEGG) decomposing the brain into some simple components with physical meaning and integrating recognition and analysis of brain activity. </p> <p><i>Approach. </i>Independent and interactive components of neurons or brain regions can fully describe the brain. This paper constructed a relationship frame based on the independent and interactive compositions for intention recognition and analysis using a novel dendrite module of Gang neurons. A total of 4,906 EEG data of left- and right-hand motor imagery(MI) from 26 subjects were obtained from GigaDB. Firstly, this paper explored EEGG’s classification performance by cross-subject accuracy. Secondly, this paper transformed the trained EEGG model into a relation spectrum expressing independent and interactive components of brain regions. Then, the relation spectrum was verified using the known ERD/ERS phenomenon. Finally, this paper explored the previously unreachable further BCIbased analysis of the brain. </p> <p><i>Main results. </i>(1) EEGG was more robust than typical “CSP+” algorithms for the poorquality data. (2) The relation spectrum showed the known ERD/ERS phenomenon. (3) Interestingly, EEGG showed that interactive components between brain regions suppressed ERD/ERS effects on classification. This means that generating fine hand intention needs more centralized activation in the brain. </p> <p><i>Significance. </i>EEGG decomposed the biological EEG-intention system of this paper into the relation spectrum inheriting the Taylor series (<i>in analogy with the data-driven but human-readable Fourier transform and frequency spectrum</i>), which offers a novel frame for analysis of the brain.</p> </div> </div></div><div><p></p></div>

scholarly journals Stimulus Intervals Modulate the Balance of Brain Activity in the Human Primary Somatosensory Cortex: An ERP Study

2021 ◽  
Vol 14 ◽  
Author(s):  
Yang Liu ◽  
Bo Dong ◽  
Jiajia Yang ◽  
Yoshimichi Ejima ◽  
Jinglong Wu ◽  
...  
Keyword(s):  
Animal Studies ◽  
Standard Stimulus ◽  
Brain Activity ◽  
Somatosensory System ◽  
Body Part ◽  
Single Stimulus ◽  
Suppression Effect ◽  
Neuronal Excitation ◽  
The Brain ◽  
Somatosensory Areas

Neuronal excitation and inhibition occur in the brain at the same time, and brain activation reflects changes in the sum of excitation and inhibition. This principle has been well-established in lower-level sensory systems, including vision and touch, based on animal studies. However, it is unclear how the somatosensory system processes the balance between excitation and inhibition. In the present ERP study, we modified the traditional spatial attention paradigm by adding double stimuli presentations at short intervals (i.e., 10, 30, and 100 ms). Seventeen subjects participated in the experiment. Five types of stimulation were used in the experiment: a single stimulus (one raised pin for 40 ms), standard stimulus (eight pins for 40 ms), and double stimuli presented at intervals of 10, 30, and 100 ms. The subjects were asked to attend to a particular finger and detect whether the standard stimulus was presented to that finger. The results showed a clear attention-related ERP component in the single stimulus condition, but the suppression components associated with the three interval conditions seemed to be dominant in somatosensory areas. In particular, we found the strongest suppression effect in the ISI-30 condition (interval of 30 ms) and that the suppression and enhancement effects seemed to be counterbalanced in both the ISI-10 and ISI-100 conditions (intervals of 10 and 100 ms, respectively). This type of processing may allow humans to easily discriminate between multiple stimuli on the same body part.

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